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Predicting Academic Achievement with Machine Learning Algorithms

Journal of Educational Technology and Online Learning, 2020-01, Vol.3 (3), p.372-392

2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2618-6586 ;DOI: 10.31681/jetol.773206

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  • Title:
    Predicting Academic Achievement with Machine Learning Algorithms
  • Author: YILDIZ, Muhammed ; BÖREKCİ, Caner
  • Subjects: Academic Achievement ; Accuracy ; Algorithms ; Artificial Intelligence ; Data Analysis ; Foreign Countries ; Grade 9 ; Grade Prediction ; High School Students ; Machine learning ; Predictor Variables
  • Is Part Of: Journal of Educational Technology and Online Learning, 2020-01, Vol.3 (3), p.372-392
  • Description: Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data mining methods. The data contains demographic information about students and their families, studying routines, behaviours of attending learning activities, and their epistemological beliefs about science. Thus, this research aimed to solve a classification problem, two-class (successful or unsuccessful according to the exam result) was tried to be estimated from the collected data. In the study, the prediction accuracy of the supervised classification algorithms were compared and it was defined which variables were effective in the formation of classes. When the prediction accuracy of machine learning algorithms was compared, the findings indicated that the Neural Network algorithm (98.6%) had the highest score. The information gain coefficient of the variables was examined to determine the factors affecting the prediction accuracy. It was revealed that demographic variables of the family, scientific epistemological beliefs of the student, study routines and attitudes towards some courses affected the classification. It can be concluded that there was a relationship between these variables and academic success. Studies on these variables will support students' academic success.
  • Publisher: Balıkesir: Gürhan Durak
  • Language: English;Turkish
  • Identifier: ISSN: 2618-6586
    DOI: 10.31681/jetol.773206
  • Source: ERIC Full Text Only (Discovery)
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